Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review

Human motor performance assessment with lower limb exoskeletons as a potential strategy to support healthy aging-a perspective article

With increasing age, motor performance declines. This decline is associated with less favorable health outcomes such as impaired activities of daily living, reduced quality of life, or increased mortality. Through regular assessment of motor performance, changes over time can be monitored, and targeted therapeutic programs and interventions may be informed. This can ensure better individualization of any intervention approach (e.g. by considering the current motor performance status of a person) and thus potentially increase its effectiveness with regard to maintaining current performance status or delaying further decline. However, in older adults, motor performance assessment is time consuming and requires experienced examiners and specific equipment, amongst others. This is particularly not feasible in care facility/nursing home settings. Wearable robotic devices, such as exoskeletons, have the potential of being used to assess motor performance and provide assistance during physical activities and exercise training for older adults or individuals with mobility impairments, thereby potentially enhancing motor performance. In this manuscript, we aim to (1) provide a brief overview of age-related changes of motor performance, (2) summarize established clinical and laboratory test procedures for the assessment of motor performance, (3) discuss the possibilities of translating established test procedures into exoskeleton-based procedures, and (4) highlight the feasibility, technological requirements and prerequisites for the assessment of human motor performance using lower limb exoskeletons.

Center of mass states render multijoint torques throughout standing balance recovery

Successful reactive balance control requires coordinated modulation of hip, knee, and ankle torques. Stabilizing joint torques arise from neurally-mediated feedforward tonic muscle activation that modulates muscle short-range stiffness, which provides instantaneous "mechanical feedback" to the perturbation. In contrast, neural feedback pathways activate muscles in response to sensory input, generating joint torques after a delay. However, the specific contributions from feedforward and feedback pathways to the balance-correcting torque response are poorly understood. As feedforward- and feedback-mediated torque responses to balance perturbations act at different delays, we modified the sensorimotor response model (SRM), previously used to analyze the muscle activation response, to reconstruct joint torques using parallel feedback loops. Each loop is driven by the same information, center of mass (CoM) kinematics, but each loop has an independent delay. We evaluated whether a torque-SRM could decompose the reactive torques during balance-correcting responses to backward support surface translations at four magnitudes into the instantaneous "mechanical feedback" torque modulated by feedforward neural commands before the perturbation and neurally-delayed feedback components. The SRM accurately reconstructed torques at the hip, knee, and ankle, across all perturbation magnitudes (R2 > 0.84 and VAF > 0.83). Moreover, the hip and knee exhibited feedforward and feedback components, while the ankle only exhibited feedback components. The lack of a feedforward component at the ankle may occur because the compliance of the Achilles tendon attenuates muscle short-range stiffness. Our model may provide a framework for evaluating changes in the feedforward and feedback contributions to balance that occur due to aging, injury, or disease.NEW & NOTEWORTHY Reactive balance control requires coordination of neurally-mediated feedforward and feedback pathways to generate stabilizing joint torques at the hip, knee, and ankle. Using a sensorimotor response model, we decomposed reactive joint torques into feedforward and feedback contributions based on delays relative to the center of mass kinematics. Responses across joints were driven by the same signals, but contributions from feedforward versus feedback pathways differed, likely due to differences in musculotendon properties between proximal and distal muscles.

Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals

Background and Objective

Exoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical potential is observed before movement in the electroencephalogram and can be used for intention prediction. However, its single-trial features are weak and highly variable, and existing methods cannot achieve high cross-temporal and cross-subject accuracies in practical online applications. Therefore, this work aimed to combine a deep convolutional neural network (CNN) framework with a transfer learning (TL) strategy to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also provide sufficient processing time for the response of the exoskeleton robotic system and help realize robot control based on the intention of the human body.

Methods

The signal characteristics of the RP for lower limb movement were analyzed, and a parameter TL strategy based on CNN was proposed to predict the intention of voluntary lower limb movements. We recruited 10 subjects for offline and online experiments. Multivariate empirical-mode decomposition was used to remove the artifacts, and the moment of onset of voluntary movement was labeled using lower limb electromyography signals during network training.

Results

The RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23% ± 1.25% for the right leg and 91.21% ± 1.48% for the left leg, which showed good cross-temporal and cross-subject generalization while greatly reducing the training time. Online movement intention prediction can predict results about 483.9 ± 11.9 ms before movement onset with an average accuracy of 82.75%.

Conclusion

The proposed method has a higher prediction accuracy with a lower training time, has good generalization performance for cross-temporal and cross-subject aspects, and is well-prioritized in terms of the temporal responses; these features are expected to lay the foundation for further investigations on exoskeleton robot control.

Center of mass states render multi-joint torques throughout standing balance recovery

Successful reactive balance control requires coordinated modulation of hip, knee, and ankle torques. Stabilizing joint torques arise from feedforward neural signals that modulate the musculoskeletal system's intrinsic mechanical properties, namely muscle short-range stiffness, and neural feedback pathways that activate muscles in response to sensory input. Although feedforward and feedback pathways are known to modulate the torque at each joint, the role of each pathway to the balance-correcting response across joints is poorly understood. Since the feedforward and feedback torque responses act at different delays following perturbations to balance, we modified the sensorimotor response model (SRM), previously used to analyze the muscle activation response to perturbations, to consist of parallel feedback loops with different delays. Each loop within the model is driven by the same information, center of mass (CoM) kinematics, but each loop has an independent delay. We evaluated if a parallel loop SRM could decompose the reactive torques into the feedforward and feedback contributions during balance-correcting responses to backward support surface translations at four magnitudes. The SRM accurately reconstructed reactive joint torques at the hip, knee, and ankle, across all perturbation magnitudes (R 2 >0.84 & VAF>0.83). Moreover, the hip and knee exhibited feedforward and feedback components, while the ankle only exhibited feedback components. The lack of a feedforward component at the ankle may occur because the compliance of the Achilles tendon attenuates muscle short-range stiffness. Our model may provide a framework for evaluating changes in the feedforward and feedback contributions to balance that occur due to aging, injury, or disease.

NEWS AND NOTEWORTHY

Reactive balance control requires coordination of neurally-mediated feedforward and feedback pathways to generate stabilizing joint torques at the hip, knee, and ankle. Using a sensorimotor response model, we decomposed reactive joint torques into feedforward and feedback contributions based on delays relative to center of mass kinematics. Responses across joints were driven by the same signals, but contributions from feedforward versus feedback pathways differed, likely due to differences in musculotendon properties between proximal and distal muscles.

Novice Users' Brain Activity and Biomechanics Change After Practicing Walking With an Ankle Exoskeleton

Walking with an exoskeleton represents a sophisticated interplay between human physiology and mechanical augmentation, yet understanding of cortical responses in this context remains limited. To address this gap, this study aimed to explore cortical responses during walking with an ankle exoskeleton, examining how these responses evolve with familiarity to the augmentation. Healthy participants without prior exoskeleton experience underwent EEG, EMG, and motion capture analysis while walking with exoskeleton assistance at 1.2m/s. Initially, exoskeleton-assisted walking induced significant biomechanical changes accompanied by corresponding cortical alterations, leading to increased cortical involvement. In addition, after a brief familiarization period, significant increases in alpha band cortical power were observed, indicating decreased cortical engagement. These findings hold significance for elucidating the cortical mechanisms involved in exoskeleton-assisted walking and may contribute to the development of more seamlessly integrated augmentation devices.

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement

Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.

Lower limb biomechanics of fully trained exoskeleton users reveal complex mechanisms behind the reductions in energy cost with human-in-the-loop optimization

Exoskeletons that assist in ankle plantarflexion can improve energy economy in locomotion. Characterizing the joint-level mechanisms behind these reductions in energy cost can lead to a better understanding of how people interact with these devices, as well as to improved device design and training protocols. We examined the biomechanical responses to exoskeleton assistance in exoskeleton users trained with a lengthened protocol. Kinematics at unassisted joints were generally unchanged by assistance, which has been observed in other ankle exoskeleton studies. Peak plantarflexion angle increased with plantarflexion assistance, which led to increased total and biological mechanical power despite decreases in biological joint torque and whole-body net metabolic energy cost. Ankle plantarflexor activity also decreased with assistance. Muscles that act about unassisted joints also increased activity for large levels of assistance, and this response should be investigated over long-term use to prevent overuse injuries.

Magnetorheological Damper With Variable Displacement Permanent Magnet for Assisting the Transfer of Load in Lower Limb Exoskeleton

Magnetorheological (MR) fluid exhibits the ability to modulate its shear state through variations in magnetic field intensity, and is widely used for applications requiring damping. Traditional MR dampers use the current in the coil to adjust the magnetic field strength, but the accumulated heat can cause the magnetic field strength to decay if it works for a long time. In order to deal with this shortcoming, a novel MR damper is proposed in this paper, which is based on a variable displacement permanent magnet to adjust the output resistance torque and applied to an exoskeleton joint for human load transfer assistance. A finite element model is used to determine the size parameters of the magnet and separator, so that the maximum output torque is optimal and the torque is uniformly distributed with the magnet displacement. The MR damper was characterized and calibrated on the experimental bench to make it controllable. The novel design enables the torque mass density of the MR damper to reach 8.83Nmm/g, the torque volume density to reach 48.7N/mm2, and has stability for long-term operation. Based on the torque control method proposed, a preliminary human experiment is conducted. The ground reaction force (GRF) data of the subjects is analyzed here, which represents the effect of load transfer to the exoskeleton. Compared with no exoskeleton, the GRF with exoskeleton is significantly reduced: the peak GRF in early stance phase is reduced by 24.14%, and in late stance phase is reduced by 19.77%. Based on our net load benefit (NLB) and net force benefit (NFB) evaluation indicators, the effectiveness of the proposed MR damper exoskeleton for human weight bearing assistance is established.

Robotic exoskeleton embodiment in post-stroke hemiparetic patients: an experimental study about the integration of the assistance provided by the REFLEX knee exoskeleton

Hemiparetic gait is the most common motor-disorder after stroke and, in spite of rehabilitation efforts, it is persistent in 50% of community dwelling stroke-survivors. Robotic exoskeletons have been proposed as assistive devices to support impaired joints. An example of these devices is the REFLEX knee exoskeleton, which assists the gait of hemiparetic subjects and whose action seems to be properly embodied by stroke survivors, who were able to adapt the motion of their non-assisted limbs and, therefore, reduce their compensation mechanisms. This paper presents an experimental validation carried out to deepen into the effects of REFLEX's assistance in hemiparetic subjects. Special attention was paid to the effect produced in the muscular activity as a metric to evaluate the embodiment of this technology. Significant differences were obtained at the subject level due to the assistance; however, the high dispersion of the measured outcomes avoided extracting global effects at the group level. These results highlight the need of individually tailoring the action of the robot to the individual needs of each patient to maximize the beneficial outcomes. Extra research effort should be done to elucidate the neural mechanisms involved in the embodiment of external devices by stroke survivors.

Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control

Lower limb robotic exoskeletons are often studied in the context of steady-state treadmill walking in laboratory environments. However, the end goal of these devices is often adoption into our everyday lives. To move outside of the laboratory, there is a need to study exoskeletons in real world, complex environments. One way to study the human-machine interaction is to look at how the exoskeleton affects the user's gait. In this study we assessed changes in gait spatiotemporal variability when using a robotic ankle exoskeleton under proportional myoelectric control both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would not lead to significant changes in variability inside on a treadmill or outside compared to not using the exoskeletons. In addition, we hypothesized that walking outside would lead to higher variability both with and without the exoskeletons compared to treadmill walking. In support of our hypothesis, we found significantly higher coefficients of variation of stride length, stance time, and swing time when walking outside both with and without the exoskeleton. We found a significantly higher variability when using the exoskeletons inside on the treadmill, but we did not see significantly higher variability when walking outside overground. The value of this study to the literature is that it emphasizes the importance of studying exoskeletons in the environment in which they are meant to be used. By looking at only indoor gait spatiotemporal measures, we may have assumed that the exoskeletons led to higher variability which may be unsafe for certain target populations. In the context of the literature, we show that variability due to robotic ankle exoskeletons under proportional myoelectric control does not elicit different changes in stride time variability than previously found in other daily living tasks (uneven terrain, load carriage, or cognitive tasks).

sEMG-Based Adaptive Cooperative Multi-Mode Control of a Soft Elbow Exoskeleton Using Neural Network Compensation

Soft rehabilitation exoskeletons have gained much attention in recent years, striving to assist the paralyzed individuals restore motor functions. However, it is a challenge to promote human-robot interaction property and satisfy personalized training requirements. This article proposes a soft elbow rehabilitation exoskeleton for the multi-mode training of disabled patients. An adaptive cooperative admittance backstepping control strategy combined with surface electromyography (sEMG)-based joint torque estimation and neural network compensation is developed, which can induce the active participation of patients and guarantee the accomplishment and safety of training. The proposed control scheme can be transformed into four rehabilitation training modes to optimize the cooperative training performance. Experimental studies involving four healthy subjects and four paralyzed subjects are carried out. The average root mean square error and peak error in trajectory tracking test are 3.18° and 5.68°. The active cooperation level can be adjusted via admittance model, ranging from 4.51 °/Nm to 10.99 °/Nm. In cooperative training test, the average training mode value and effort score of healthy subjects (i.e., 1.58 and 1.50) are lower than those of paralyzed subjects (i.e., 2.42 and 3.38), while the average smoothness score and stability score of healthy subjects (i.e., 3.25 and 3.42) are higher than those of paralyzed subjects (i.e., 1.67 and 1.71). The experimental results verify the superiority of proposed control strategy in improving position control performance and satisfying the training requirements of the patients with different hemiplegia degrees and training objectives.

Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors

Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.

Neuromechanical Adaptation to Walking With Electromechanical Ankle Exoskeletons Under Proportional Myoelectric Control

OBJECTIVE

To determine if robotic ankle exoskeleton users decrease triceps surae muscle activity when using proportional myoelectric control, we studied healthy young participants walking with commercially available electromechanical ankle exoskeletons (Dephy Exoboot) with a novel controller. The vast majority of robotic lower limb exoskeletons do not have direct neural input from the user which makes adaptation of exoskeleton dynamics based on user intent difficult. Proportional myoelectric control has proven to allow considerable adaptation in muscle activation and gait kinematics in pneumatic, tethered ankle exoskeletons. In this study we quantified the changes in muscle activity and joint biomechanics of twelve participants walking for 30 minutes on a treadmill.

RESULTS

The exoskeletons provided 29% of the peak total ankle power and 18% of the peak total ankle moment by the end of the practice session. There was a decrease of 12% in soleus, 17% in lateral gastrocnemius and 5% in medial gastrocnemius electromyography (EMG) root mean square (root mean squared) after walking with the exoskeleton for 30 minutes compared to not wearing the exoskeleton, but this difference was not statistically significant. There were no differences in joint biomechanics of the ankle, hip, or knee between the end of training compared to walking without the exoskeletons.

CONCLUSIONS

Contrary to expectations, triceps surae muscle activity showed only small non-significant decreases in 30 minutes of walking with portable, electromechanical ankle exoskeletons under proportional myoelectric control. The commercially available ankle exoskeletons were likely too weak to produce a statistically meaningful decline in triceps surae recruitment. Future research should include a wider variety of tasks, including measurements of metabolic energy expenditure, and provide a longer period of adaptation to evaluate the ankle exoskeletons.